In silico and in vitro evaluation of the anti-virulence potential of patuletin, a natural methoxy flavone, against Pseudomonas aeruginosa

This study aimed to investigate the potential of patuletin, a rare natural flavonoid, as a virulence and LasR inhibitor against Pseudomonas aeruginosa. Various computational studies were utilized to explore the binding of Patuletin and LasR at a molecular level. Molecular docking revealed that Patuletin strongly interacted with the active pocket of LasR, with a high binding affinity value of −20.96 kcal/mol. Further molecular dynamics simulations, molecular mechanics generalized Born surface area (MM/GBSA), protein-ligand interaction profile (PLIP), and essential dynamics analyses confirmed the stability of the patuletin-LasR complex, and no significant structural changes were observed in the LasR protein upon binding. Key amino acids involved in binding were identified, along with a free energy value of −26.9 kcal/mol. In vitro assays were performed to assess patuletin’s effects on P. aeruginosa. At a sub-inhibitory concentration (1/4 MIC), patuletin significantly reduced biofilm formation by 48% and 42%, decreased pyocyanin production by 24% and 14%, and decreased proteolytic activities by 42% and 20% in P. aeruginosa isolate ATCC 27853 (PA27853) and P. aeruginosa clinical isolate (PA1), respectively. In summary, this study demonstrated that patuletin effectively inhibited LasR activity in silico and attenuated virulence factors in vitro, including biofilm formation, pyocyanin production, and proteolytic activity. These findings suggest that patuletin holds promise as a potential therapeutic agent in combination with antibiotics to combat antibiotic-tolerant P. aeruginosa infections.


Molecular Dynamic (MD) Simulation
A standard unbiased MD simulation lasting 100 ns was performed in GROMACS 2021 to assess the stability of the Protein Plateulin complex and to analyze the structural changes that occur between the apo and holo forms of the protein (1).We utilized the solution builder module of the CHARMM-GUI server to generate the input files (2)(3)(4)(5).The apo protein and the docked complex were both solvated in an 8.5 nm long cubic box.After solvating the system using the transferable intermolecular potential 3 points (TIP3P) water model with a padding of 1 nm from the furthest distant atom, the system was neutralized by adding NaCl ions at a concentration of 0.154 M. The CHARMM36m force field was used to obtain the amino acid parameters of the protein, as well as those of the TIP3P water model and the neutralizing ions.
The Plateulin molecule was parameterized using the CHARMM general force field (CGenFF).
Periodic boundary conditions (PBC) were used in all three dimensions throughout the simulation.To prevent atomic collisions, the potential energy was minimized using the steepest descent algorithm.Following that, the temperature and pressure in the systems were equilibrated in two steps.When the maximum force applied to any atom was less than 100 KJ/(mol.nm), or when the number of reduction steps reached 100,000, the minimizing process was regarded to be converged.The NVT ensemble and the Velocity Rescale technique were utilized to reach an average temperature of 310 K during the first phase of the equilibration process.The NPT ensemble, the Berendsen barostat, and the velocity rescale algorithms were utilized in the second phase to maintain an atmospheric pressure of 1 atm and an average temperature of 310 K (6).For the 100 ns production run, an NPT ensemble was utilized, and the temperature and pressure were regulated by a Nose-Hoover thermostat and a Parrinello-Rahman barostat, respectively.The temperature was kept at 310 degrees Celsius, while the pressure was maintained at one atmospheric pressure (7).The LINear Constraint Solver (LINCS) was used to impose length constraints on the hydrogen-bonded atoms (8).
We utilized Particle Mesh Ewald (PME) to calculate the electrostatics with a threshold of 1.2 nm (9).By using a time step of 1 femtosecond during equilibration and 2 femtoseconds throughout the production run, the Newtonian equations of motion were integrated using the leap-frog approach.We took 1,000 frames at 0.1 ns intervals during the simulation.After the PBC was removed from the system (making the protein whole again) using the trjconv command, we used VMD TK scripts to analyze the trajectory (10).The RMSDs for Protein and Plateulin were calculated.We also quantified the root mean square fluctuation (RMSF), the radius of gyration (RoG), solvent accessible surface area (SASA), change in hydrogen bond count, and ligand-to-protein center of mass distance to characterize the differences between the apo and holo protein structure and the stability of the compound.
Protein-Ligand Interaction Fingerprints (ProLIF) is a Python program that we used to discover which interacting amino acids were most crucial to the binding stability by counting and characterizing the quantity and kind of ligand-amino acid interactions in each frame (11).The protein-ligand complex trajectory was then clustered using TTclust to get representative frames for each cluster.Protein-Ligand interaction profiler (PLIP) was used to determine the number and kind of interactions taking place in these snapshots, and the results were written to a .psefile that can be viewed in PyMol's to observe the 3D conformation of the binding (12,13).

Binding free energy calculation using MM-GBSA:
The binding energy of the ligand was determined using the MM-GBSA strategy using the gmx_MMPBSA software.Decomposition analysis was also used to determine the relative importance of each amino acid within 1 nm of the ligand to the total binding (14,15).
A value of 0.154 M for the ionic strength, and a value of 5 for the solvation technique (igb), were chosen.the internal dielectric constant was set to 1.0, whereas it was 78.5 for the external dielectric.The MM-GBSA method can be represented by equation 1. ∆G = < Gcomplex -(Greceptor + Gligand)> Equation 1Where < > represents the average of the enclosed free energies of complex, receptor, and ligand over the frames used in the calculation.In our approach, we used the part of the trajectory showing stable ligand RMSD (the last 400 frames).Different energy terms can be calculated ∆H is the enthalpy which can be calculated from gas-phase energy (Egas) and solvation-free energy (Esol).-T∆S is the entropy contribution to the free binding energy.Egas is composed of electrostatic and van der Waals terms; Eele, EvdW, respectively.Esol can be calculated from the polar solvation energy (EGB) and nonpolar solvation energy (ESA) which is estimated from the solvent-accessible surface area (16,17).

Principal Component Analysis:
Principal component analysis (PCA) of the mass-weighted covariance matrix (C) of atoms in MD trajectories may show coordinated motion.Therefore, we employed principal component analysis to assess the mobility of alpha carbons (18).For the alignment step during studying a single trajectory, the last frame of each trajectory's equilibrium stage was used.The apo system's final frame after it has attained equilibrium was used as the reference structure to examine the combined trajectories.With the aid of the gmx covar command, the C matrix in GROMACS was diagonalized, and the analysis was performed with the assistance of the gmx anaeig command.
The following steps were used to determine the size of the crucial subspace: 1) summing up the eigenvalues with every additional eigenvector (cumulative sum).2) When plotting eigenvalues at each eigenvector index (scree plot), the point with the steepest decline in slope is selected.3) Given that non-random eigenvectors do not follow a Gaussian distribution, the distribution of the eigenvectors was studied.
We computed the cosine content (ci) of each eigenvector of the C matrix, which may take values ranging from 0 (no cosine) to 1 (perfect cosine).The following is the cosine content equation: Where T is the time of the simulation.Abnormally large ci values, which represent random motion, are related to insufficient sampling.When the cosine content of the first few PCs is near 1, the behavior of proteins on a large scale is analogous to diffusion.Accordingly, the first 10 PCs were used to calculate their cosine content (19)(20)(21).
We were able to directly compare the frames in the reduced essential subspace by aligning the combined apo-protein and complex trajectories to the apo-protein configuration obtained after equilibration, generating a new C matrix for the combined trajectories, and then projecting each trajectory onto the new C matrix.

Bacterial isolates
In overnight before the bacterial suspensions were diluted using MHB to achieve a cell density of 0.5 McFarland Standard (10 7 CFU/ml).The tested substance, patuletin, was utilized to prepare two-fold serial dilution solutions.On microtiter plates, 100 μl of the tested bacterial suspensions were mixed with 100 μl of the prepared diluted tested substance.The lowest concentration of the tested substance that prevented bacterial growth that was observable after an overnight incubation at 37°C was determined to be the MIC value.

Biofilm inhibition assay
According to Stepanovi et al. (2007) procedures, the ability of the tested isolates to generate biofilms was evaluated.P. aeruginosa suspensions were prepared from cultures that had been grown in tryptone soy broth (TSB) overnight and then diluted to the 0.5 McFarland standard.
The prepared suspensions were diluted 1:100 in new TSB with 1% glucose addition.The wells of microtiter plates were filled with aliquots of 200 μl of diluted suspensions, which were then incubated for 48 hours at 37°C in the presence and absence of sub-MIC of patuletin.Each plate included negative-control wells that contained 200 μl of fresh TSB supplemented with 1% glucose alone.After incubation, the well contents were decanted, and the plates underwent three water washes to remove any remaining planktonic cells before being allowed to air dry.
To fix the biofilms, 150 μl of 99% methanol were applied in aliquots and left for 20 min.The biofilms were then stained for 15 minutes with aliquots of 150 μl of crystal violet (1%) before being rinsed three times with water and dried.The bound dye was solubilized with 150 μl of 33% glacial acetic acid.The OD570 was measured using spectrofluorometer (Biotek, USA).
The experiment was done in triplicate.The biofilm forming capacity was assessed according to the criteria of Stepanović et al. (2007).From the following formula, the percentage (%) of biofilm inhibition was calculated: Percentage of biofilm inhibition = [(ControlOD570nm _ TreatedOD570nm)/ ControlOD570nm x 100

Pyocyanin inhibition assay
Pyocyanin estimation was carried out following Das and Manefield (2012) procedures.Using overnight cultures of the tested isolates in Luria-Bertani (LB) broth that had been diluted to an OD600 of 0.3-0.4,pyocyanin was quantified.In both the presence and absence of sub-MIC of patuletin, 50 μl of the diluted cultures of each isolate were added to 5 ml of LB broth.The cultures were centrifuged at 10,000 rpm for 10 minutes at 4°C after being incubated at 37°C for 48 hours and the pyocyanin pigment in the supernatants was measured directly at 691 nm using spectrofluorometer (Biotek, USA).

Proteases inhibition assay
According to El-Mowafy et al. 2014, the modified skim milk technique was used to assess the patuletin ability to suppress the production of proteases.In brief, P. aeruginosa isolates were cultured overnight in LB broth with and without sub-MIC of patuletin.Supernatants were produced by centrifuging bacterial suspensions at 10,000 rpm for 10 minutes.The next step was to mix 0.5 ml of each tested isolate cell-free supernatant with 1 ml of skim milk solution (1.25% in distilled water), which was then incubated for ½ hr at 37 °C.Using a spectrofluorometer (Biotek, USA) the turbidities of assay solutions were assessed at OD600 as a measure of proteolytic activity.

Statistical analysis
The GraphPad Prism 7 software package was used to analyze the data for the current study.
The effect of patuletin on P. aeruginosa virulence factors was compared using One Way ANOVA at P > 0.05 for significance.The results were computed using the means and standard errors of three biological tests with three technical replicates.

Inhibitors
according to Equations 2 to 6 as follows: ∆Gbinding = ∆H -T∆S Equation ∆H = ∆Egas + ∆Esol Equation ∆Egas = ∆Eele + ∆EvdW Equation 4 ∆Esolv = EGB + ESA Equation ESA = γ.SASA EquationWhere: the current study, one clinical P. aeruginosa isolate (PA1) and a standard P. aeruginosa isolate ATCC 27853 (PA27853) were used.The clinical isolate was obtained from the stock culture collection of the Microbiology and Immunology Department at Faculty of Pharmacy, Port Said University, while the standard isolate was a kind gift from Microbiology and Immunology Department, Faculty of Pharmacy, Suez Canal University.Using the broth microdilution technique in accordance with Clinical and Laboratory Standards Institute (CLSI) procedures (Patel et al., 2015), the MIC of patuletin was assessed.Briefly, a single colony of each of the tested isolates was cultured in Mueller-Hinton broth (MHB)